Margaret Atwood’s latest comments on artificial intelligence didn’t arrive as a sweeping condemnation or a futuristic prophecy. They came, instead, from a very ordinary place: a single attempt to use a chatbot for something specific, followed by the kind of disappointment that only happens when you expected a straightforward answer and got something else entirely.
Atwood—best known for The Handmaid’s Tale and The Blind Assassin—was interviewed at the Babell Literary and Cultural Festival in Porto, Portugal. As often happens at cultural events where writers and technologists share the same stage, AI inevitably entered the conversation. But what made her remarks stand out wasn’t the usual back-and-forth about whether AI will “replace” writers or whether it will “transform” creativity. Her point was more grounded, more practical, and arguably more unsettling: she described how the system failed her in a way that illustrates a broader weakness in today’s AI tools.
According to reporting from Deadline, Atwood said she used an AI chatbot exactly once. The model she tried was Anthropic’s Claude. She was looking for information about Father Brown, a British detective series. The expectation was simple: ask a question, receive an answer that matches reality, and move on with your day. Instead, she said the chatbot gave her the wrong answer—or, in her words, lied. And then she added a detail that reframes the entire issue. The system didn’t “know” it was lying in the human sense, because it isn’t a person. It’s a large language model, built to generate plausible text rather than to guarantee truth.
That distinction matters, because it points to a problem that many people experience but rarely hear articulated so plainly. When a human gives you incorrect information, you can at least imagine intent, misunderstanding, or bias. When a chatbot gives you incorrect information, it can feel like a betrayal even though the mechanism behind it is different. The output may be fluent, confident, and well-structured—qualities that mimic credibility—while still being wrong. Atwood’s reaction captures the emotional side of a technical failure: the sense that the tool is not merely mistaken, but convincingly mistaken.
Her explanation also gestures toward another familiar phenomenon: the way these systems can “skim” information. In her account, the chatbot seemed to have pulled from fragments without assembling them into the correct context. That’s not just a matter of missing facts; it’s a matter of coherence. A model can retrieve or generate details that sound related while failing to connect them to the actual answer you asked for. The result is a response that reads like knowledge but behaves like guesswork.
This is where Atwood’s “garbage in, garbage out” framing becomes more than a slogan. The phrase is often used casually to imply that bad inputs produce bad outputs. But in the case of modern AI, the “inputs” aren’t always what users think they are. People assume the input is their question. Yet the real inputs include the training data the model absorbed years earlier, the patterns it learned from that data, and the way it interprets your prompt in the moment. Even if your question is clear, the system’s internal assumptions may lead it toward an answer that is statistically likely to resemble what you want—rather than what is actually true.
To understand why this happens, it helps to think of a chatbot less like a search engine and more like a text generator trained to predict what comes next. When you ask for information, the model doesn’t “look up” a verified fact in the way a database would. It produces a response by drawing on learned associations between words and concepts. If those associations are incomplete, outdated, or simply not aligned with the specific query you asked, the model can still produce something that feels right. Fluency becomes a substitute for accuracy.
Atwood’s example—asking about a specific TV series—may seem small, but it’s precisely the kind of scenario where the mismatch between fluency and truth becomes obvious. With broad topics, a chatbot can offer a general overview that sounds credible even if it’s not perfectly accurate. With narrower questions, the margin for error shrinks. A wrong title, a misremembered plot detail, or a conflation with a similarly named show can turn a helpful response into a misleading one. And because the model is designed to be helpful, it will often fill the gap with something that resembles the correct answer closely enough to pass casual scrutiny.
That’s why Atwood’s comment lands with particular force for readers and writers. Writers are trained to notice when something doesn’t add up. They also know how easily a story can be made to look convincing while being fundamentally wrong. In fiction, you can get away with invented details because the contract with the reader is different. In information retrieval, the contract is supposed to be truth. When a chatbot breaks that contract, it doesn’t just provide misinformation—it undermines trust in the process.
There’s another layer to her remarks that deserves attention: the idea that the system doesn’t “know” it’s lying. This is not a defense of AI; it’s a clarification of what “lying” means in a human context. Humans lie intentionally or at least knowingly. A model generates text based on patterns. It can produce confident statements without having any internal awareness of correctness. That’s why the same system can sometimes be right and sometimes be wrong, and why the user may not be able to tell which is which. The model’s confidence is not evidence of truth; it’s evidence of how it has learned to respond.
In other words, the danger isn’t only that AI can be inaccurate. The danger is that it can be inaccurate in a way that looks like accuracy. This is the part that makes Atwood’s experience feel like a warning rather than a complaint. She didn’t say the chatbot was unusable. She said it gave her the wrong answer or lied. That implies a level of reliability failure that goes beyond minor errors. It suggests that the system can cross the line from “imperfect” to “misleading,” especially when the user expects a direct factual match.
So what does “garbage in, garbage out” really mean in this context? It means that the quality of the output is constrained by the quality of the system’s underlying knowledge and its ability to align that knowledge with the user’s request. If the model’s training data contains inaccuracies, if it lacks coverage for certain niche topics, if it confuses similar entities, or if it fails to interpret the prompt correctly, the output will reflect those weaknesses. The user’s question may be clean, but the system’s internal representation of the world may not be.
And there’s a second meaning, equally important: even if the model’s training data were perfect, the user’s interpretation of the output can still be “garbage in.” People tend to treat a chatbot’s response as a finished product. They may not verify it. They may not cross-check sources. They may not notice subtle errors. The model’s output becomes the input to the next step—whether that step is writing an article, making a decision, or building an argument. At that point, the “garbage” spreads, not because the model is malicious, but because the workflow assumes reliability.
This is why Atwood’s comments resonate with a broader cultural anxiety about AI. The fear isn’t only that AI will generate falsehoods. It’s that society is learning to accept them quickly, because the interface is designed to be smooth. A chatbot doesn’t come with the friction of verification. It offers answers immediately, in natural language, with no visible uncertainty. That makes it easy to treat the response as authoritative even when it isn’t.
The Verge story referenced in the discussion frames Atwood’s remarks as a critique of AI’s tendency to produce wrong answers. But the deeper story is about how we evaluate information in the age of conversational machines. Traditional tools—books, journalists, academic papers—carry signals of credibility: citations, editorial standards, reputational incentives, and the ability to trace claims back to sources. Chatbots often provide none of that by default. They can cite things, but citations are not the same as verification. A citation can be wrong, fabricated, or irrelevant. The user may not have the means or time to check.
Atwood’s experience is therefore not just about one chatbot. It’s about the mismatch between what people want from AI and what AI is built to do. People want a reliable assistant. Many models are built to be a persuasive conversational partner. Those goals overlap sometimes, but they diverge when accuracy is required. A persuasive partner can still be wrong. A reliable assistant must be right—or at least must clearly communicate uncertainty.
This is where the conversation often gets stuck in extremes. Some people argue that AI is “just a tool,” and that users should verify everything. Others argue that verification is too burdensome and that AI should be trusted by default. Atwood’s remarks suggest a third approach: treat AI as a generator of possibilities, not a generator of truth. Use it to explore, to brainstorm, to rephrase, to structure questions. But don’t outsource factual certainty to it—especially not for narrow, verifiable details.
For writers, this distinction can be both liberating and challenging. Liberating, because AI can help with drafts, outlines, and stylistic experimentation. Challenging, because writers also know that style can mask errors. A beautifully written paragraph can still contain false premises. A compelling summary can still omit crucial context. When AI produces text that reads like expertise, it can tempt the user to stop thinking critically. Atwood’s reaction—her immediate sense that the chatbot had given her the wrong answer—reads like a refusal to let that temptation win.
It’s also worth noting that Atwood’s comments come from someone who understands narrative and language deeply. She isn’t speaking as a technophobe. She’s speaking as a professional reader and writer who recognizes the difference between language that sounds right and language that is right. Her critique is, in a sense, literary: she is pointing